Abstract In the clinical diagnosis of diabetic retinopathy (DR), fluorescein angiography (FA) is currently the only method used routinely for the detection and treatment of ischemia, but it is an invasive method and not sensitive to early microvascular changes before the onset of visual symptoms. For the early detection and management of ischemia in DR, Optical Coherence Tomography Angiography (OCTA) is a novel method that has obtained FDA approval in 2015. Compared with FA, OCTA allows non-invasive imaging of both the neurosensory retina as well as the retinal vasculature at ~10?m resolution. Some recent studies have successfully used OCTA to extract quantitative 2D metrics that are well correlated with DR severity. Dr. Kashani?s group has pioneered several of the 2D OCTA metrics for the quantitative assessment of capillary morphology and density. While highly valuable, these 2D OCTA metrics were derived from the en face projection of the 3D OCTA data, and therefore inevitably obscure the geometric and topological information in the original 3D vasculature networks. To overcome this fundamental limitation, Drs. Shi and Kashani will collaborate in this R21 project to develop truly 3D metrics for the automated analysis of OCTA data and apply them for the early diagnosis of DR in large scale eye studies. For brain mapping research, Dr. Shi?s group at Laboratory of Neuro Imaging (LONI) of USC has developed various advanced computational tools for 3D shape analysis based on generally applicable principles from intrinsic geometry. In this project, we will translate and adapt these tools for 3D retinal vasculature modeling and analysis using OCTA data. There are three specific aims in this project: (1) Apply cutting-edge computational algorithms developed in brain imaging to develop novel 3D OCTA metrics for volume- and surface-based quantitation of retinal capillary density and morphology. (2) Define the relationship and reliability of 2D- and 3D-OCTA metrics with clinical severity of DR using our previously published cohorts of healthy and diabetic subjects. (3) Validate the relationship of 2D- and 3D-OCTA metrics with DR severity among a well-characterized population from the NEI funded African American Eye Diseases Study (AFEDS) and identify OCTA metrics associated with currently undetectable (sub-clinical) retinopathy. Given the rich computational tools developed by Dr. Shi?s group at LONI and the already collected, large-scale OCTA data (n=396) from the AFEDS study and Dr. Kashani?s published studies, the risk of this project is low, but the resulting 3D OCTA metrics and associated software tools will be highly valuable to the research and potentially clinical community. We will make all the software tools and source codes developed in this project freely available through the NITRC (http://www.nitrc.org) and LONI website (http://www.loni.usc.edu/Software).